This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

As of August 2020, the S&P 500 index had lost ** percent of its value due to the COVID-19 pandemic. However, the Great Crash, which began with Black Tuesday, remains the most significant loss in value in its history. That market crash lasted for 300 months and wiped ** percent off the index value.

The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.

Data used in the paper "The emergence of critical stocks in market crash".1.
The '2015bipartite.graphml' and '2015-1_fund_stock.graphml' contains the stock networks established by the mutual funds holding data on Jun 30, 2015. While the first file has the mutual funds holding values grouped by the labels of mutual fund companies, the second one uses mutual funds holding values directly. The original data of mutual funds holding are provided by Wind Information, which is not publicly available due to Wind’s license requirement.

1. The ‘stock_style.csv’ describes which kind of investment style a stock belongs to, which is also downloaded from Wind Information.

2. The series of files named as ‘first to low *.csv’ includes the stocks which reach their limit down prices. The timing of stocks reaching limit down prices are calculated from the intraday price data provided by Thomson Reuters’ Tick History. The information of whether a stock reached its limit down price is provides by Wind Information. The original price trends data is not publicly available due to the company’s license requirement.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nifty 50: Climb or Crash?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset is about books. It has 11 rows and is filtered where the book subjects is Stock Market Crash, 1929. It features 9 columns including author, publication date, language, and book publisher.

Crashes have fascinated and baffled many canny observers of financial markets. In the strict orthodoxy of the efficient market theory, crashes must be due to sudden changes of the fundamental valuation of assets. However, detailed empirical studies suggest that large price jumps cannot be explained by news and are the result of endogenous feedback loops. Although plausible, a clear-cut empirical evidence for such a scenario is still lacking. Here we show how crashes are conditioned by the market liquidity, for which we propose a new measure inspired by recent theories of market impact and based on readily available, public information. Our results open the possibility of a dynamical evaluation of liquidity risk and early warning signs of market instabilities, and could lead to a quantitative description of the mechanisms leading to market crashes.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

In complex systems like financial market, risk tolerance of individuals is crucial for system resilience. The single-security price limit, designed as risk tolerance to protect investors by avoiding sharp price fluctuation, is blamed for feeding market panic in times of crash. The relationship between the critical market confidence which stabilizes the whole system and the price limit is therefore an important aspect of system resilience. Using a simplified dynamic model on networks of investors and stocks, an unexpected linear association between price limit and critical market confidence is theoretically derived and empirically verified in this paper. Our results highlight the importance of relatively “small” but critical stocks that drive the system to collapse by passing the failure from periphery to core. These small stocks, largely originating from homogeneous investment strategies across the market, has unintentionally suppressed system resilience with the exclusive increment of individual risk tolerance. Imposing random investment requirements to mitigate herding behavior can thus improve the market resilience.

Car Crash Simulator Systems and Services Market Outlook

The global car crash simulator systems and services market size is projected to grow from USD 1.5 billion in 2023 to USD 3.2 billion by 2032, driven by a compound annual growth rate (CAGR) of 8.5%. This growth is primarily attributed to the increasing focus on automotive safety standards, technological advancements, and the rising demand for sophisticated testing and training solutions in the automotive industry.

One of the primary growth factors propelling the car crash simulator systems and services market is the stringent enforcement of safety regulations and standards across various countries. Governments and regulatory bodies are continuously working towards enhancing vehicle safety to reduce the number of road accidents and fatalities. This has led to a higher demand for advanced crash simulation systems that can accurately replicate real-world crash scenarios, thereby assisting automotive manufacturers in meeting these stringent safety norms and improving vehicle safety features.

Technological advancements in simulation software and hardware are significantly contributing to the market's growth. The integration of artificial intelligence (AI), machine learning, and virtual reality (VR) in crash simulation systems has revolutionized the way crash tests are conducted. These technologies enable more precise and realistic simulations, providing detailed insights into vehicle behavior during collisions. This has not only improved the accuracy of crash test results but also reduced the need for physical prototypes, saving time and costs for automotive manufacturers.

The rising demand for electric and autonomous vehicles is another crucial factor driving market growth. As the automotive industry shifts towards electrification and automation, the complexity of vehicle systems has increased, necessitating more comprehensive testing solutions. Car crash simulation systems play a vital role in testing the safety and performance of these advanced vehicles, ensuring they meet safety standards before they hit the market. This trend is expected to further boost the demand for car crash simulator systems and services in the coming years.

The development and utilization of Automotive Simulation Models ASM have become pivotal in the automotive industry, especially in the realm of crash simulations. These models provide a virtual environment that replicates real-world driving conditions, allowing engineers to test various scenarios and vehicle responses without the need for physical prototypes. By employing ASM, manufacturers can identify potential safety issues early in the design process, leading to more robust and safer vehicles. This not only enhances the safety features of vehicles but also accelerates the development process, ensuring that new models meet regulatory standards efficiently.

Regionally, North America and Europe are expected to dominate the car crash simulator systems and services market due to the presence of major automotive manufacturers and stringent safety regulations. However, the Asia Pacific region is anticipated to witness the fastest growth, driven by the rapid expansion of the automotive industry and increasing investments in research and development activities. The growing awareness about vehicle safety and the rising adoption of advanced simulation technologies in countries like China, Japan, and India are expected to fuel the market's growth in this region.

Component Analysis

In the component segment, the market is divided into software, hardware, and services. The software segment is expected to hold a significant share of the market due to the continuous advancements in simulation software technologies. These advancements have enabled the development of highly sophisticated and accurate simulation tools that can mimic real-world crash scenarios with great precision. The integration of AI and VR into simulation software has further enhanced its capabilities, making it an indispensable tool for automotive safety testing and research.

The hardware segment, which includes crash dummies, sensors, and testing rigs, is also expected to witness substantial growth. The demand for high-quality crash dummies and sensors that can accurately measure the impact forces during a crash is on the rise. These hardware components are crucial for conducting realistic crash tests and gathering accurate data, which is ess

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

Automotive Crash Test Barrier Market Outlook

The global market size for automotive crash test barriers is experiencing a substantial growth trajectory, projected to expand from $1.5 billion in 2023 to an estimated $2.5 billion by 2032, at a compound annual growth rate (CAGR) of approximately 6.5%. This growth is primarily driven by the increasing emphasis on vehicle safety regulations and the rising demand for advanced testing equipment to improve vehicular safety standards. With road safety becoming a critical focus globally, the automotive crash test barrier market is poised to witness significant advancements in the coming years.

One of the major growth factors in the automotive crash test barrier market is the stringent safety regulations implemented by governments and regulatory bodies worldwide. As road safety concerns escalate, governments are mandating comprehensive safety tests for vehicles, which include rigorous crash testing protocols. These regulations compel automotive manufacturers to invest in sophisticated testing facilities, thereby driving the demand for crash test barriers. Additionally, the growing consumer awareness regarding the importance of vehicle safety is urging manufacturers to comply with these standards, further boosting the market growth.

Technological advancements in crash testing methodologies are another significant growth driver for the market. The integration of sophisticated technologies, such as sensor-based testing systems and simulation software, has revolutionized the crash testing process, offering more accurate and reliable safety assessments. These innovations are encouraging manufacturers and testing facilities to upgrade their existing systems, thereby fuelling the demand for advanced crash test barriers. Furthermore, the advent of autonomous vehicles and electric cars necessitates specialized testing equipment, creating new avenues for market expansion.

The increasing focus on reducing vehicular fatalities and injuries is also propelling the growth of the automotive crash test barrier market. With road accidents being a leading cause of death globally, there is an urgent need to enhance vehicle safety features. Crash test barriers play a pivotal role in assessing the impact resistance of vehicles, enabling manufacturers to design safer automobiles. This growing emphasis on minimizing road fatalities and injuries is encouraging continuous investments in crash testing infrastructure, thereby augmenting market growth.

Crash Simulation Systems have become an integral part of modern automotive safety testing, allowing manufacturers to predict and analyze the impact of collisions on vehicle structures and occupants. These systems utilize advanced computational models to simulate crash scenarios, providing valuable insights into vehicle performance under various conditions. By incorporating real-world data and sophisticated algorithms, crash simulation systems enable engineers to optimize vehicle designs for enhanced safety. As the automotive industry continues to evolve, the role of crash simulation systems is becoming increasingly important in ensuring compliance with stringent safety standards and reducing development costs.

Regionally, North America and Europe are expected to dominate the automotive crash test barrier market due to their well-established automotive industries and stringent safety regulations. However, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period. The rapid expansion of the automotive sector in countries like China and India, coupled with the increasing adoption of safety norms, is driving the demand for crash test barriers in this region. Additionally, the presence of numerous automotive manufacturers and testing facilities in these regions is further contributing to market growth.

Type Analysis

When analyzing the market by type, the segment comprising frontal impact barriers holds significant importance, primarily due to the critical nature of frontal collisions in vehicular accidents. Frontal impact barriers are designed to simulate head-on collisions, offering insights into the vehicle's ability to absorb impact and protect its occupants. As frontal collisions are among the most common and deadly types of road accidents, testing facilities and manufacturers are increasingly focusing on enhancing their frontal impact testing capabilities. This focus is driving the demand for high-quality frontal impact barriers, propelling t

This dataset provides information on motor vehicle operators (drivers) involved in traffic collisions occurring on county and local roadways. The dataset reports details of all traffic collisions occurring on county and local roadways within Montgomery County, as collected via the Automated Crash Reporting System (ACRS) of the Maryland State Police, and reported by the Montgomery County Police, Gaithersburg Police, Rockville Police, or the Maryland-National Capital Park Police. This dataset shows each collision data recorded and the drivers involved. Please note that these collision reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the collision data available on this web page may reflect: -Information not yet verified by further investigation -Information that may include verified and unverified collision data -Preliminary collision classifications may be changed at a later date based upon further investigation -Information may include mechanical or human error This dataset can be joined with the other 2 Crash Reporting datasets (see URLs below) by the State Report Number. * Crash Reporting - Incidents Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf * Crash Reporting - Non-Motorists Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5 Update Frequency : Weekly

This data set contains crash data for many years from the Pennsylvania Department of Transportation (Penn DOT). This is a subset of the annual Crash Data compiled and released by Penn DOT for the entire state.

The Pedestrian Crash Data Study (PCDS) collected detailed data on motor vehicle vs pedestrian crashes.

View crash information from the last five years to current date.This dataset includes crashes in the Town of Cary for the previous four calendar years plus the current year to date. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, allowing for additions, deletions and modifications at any time, resulting in more accurate information in the database. Due to ongoing and continuous data entry, the numbers of records in subsequent extractions are subject to change.About Crash DataThe Cary Police Department strives to make Crash data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. As the data is updated on this site there will be instances of adding new incidents and updating existing data with information gathered through the investigative process.Not surprisingly, Crash data become more accurate over time, as new crashes are reported and more information comes to light during investigations.This dynamic nature of Crash data means that content provided here today will probably differ from content provided a week from now. Likewise, content provided on this site will probably differ somewhat from crime statistics published elsewhere by the Town of Cary, even though they draw from the same database.About Crash LocationsCrash locations reflect the approximate locations of the crash. Certain crashes may not appear on maps if there is insufficient detail to establish a specific, mappable location.This data is updated daily.

Highway crash cushions are installed along highways to protect vehicles and their occupants in the event of a crash. The market for highway crash cushions is expected to grow significantly in the coming years due to increasing traffic congestion, the growing number of vehicles on the road and the increasing number of fatal crashes. The market is also expected to be driven by government regulations requiring the installation of crash cushions on new and existing highways. The market for highway crash cushions is segmented by application, type, and region. By application, the market is divided into highways, roads, and bridges. By type, the market is divided into end terminals, redirective terminals, and attenuators. By region, the market is divided into North America, South America, Europe, Asia Pacific, and Middle East & Africa.

The 3 Crash Impact Simulator report provides a detailed analysis of emerging investment pockets, highlighting current and future market trends. It offers strategic insights into capital flows and market shifts, guiding investors toward growth opportunities in key industry segments and regions.

Lehman Brothers, the fourth largest investment bank on Wall Street, declared bankruptcy on the 15th of September 2008, becoming the largest bankruptcy in U.S. history. The investment house, which was founded in the mid-19th century, had become heavily involved in the U.S. housing bubble in the early 2000s, with its large holdings of toxic mortgage-backed securities (MBS) ultimately causing the bank's downfall. The bank had expanded rapidly following the repeal of the Glass-Steagall Act in 1999, which meant that investment banks could also engage in commercial banking activities. Lehman vertically integrated their mortgage business, buying smaller commercial enterprises that originated housing loans, which allowed the bank to expand its MBS holdings. The downfall of Lehman and the crash of '08 As the U.S. housing market began to slow down in 2006, the default rate on housing loans began to spike, triggering losses for Lehman from their MBS portfolio. Lehman's main competitor in mortgage financing, Bear Stearns, was bought by J.P. Morgan Chase in order to prevent bankruptcy in March 2008, leading investors and lenders to become increasingly concerned about the bank's financial health. As the bank relied on short-term funding on money markets in order to meet its obligations, the news of its huge losses in the third-quarter of 2008 further prevented it from funding itself on financial markets. By September, it was clear that without external assistance, the bank would fail. As its losses from credit default swaps mounted due to the deepening crash in the housing market, Lehman was forced to declare bankruptcy on September 15, as no buyer could be found to save the bank. The collapse of Lehman triggered panic in global financial markets, forcing the U.S. government to step in and bail-out the insurance giant AIG the next day on September 16. The effects of this financial crisis hit the non-financial economy hard, causing a global recession in 2009.